Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search
This addresses efficiency and scalability issues in planning for AI and robotics, though it appears incremental as an enhancement to hierarchical planning methods.
The paper tackled complex reasoning problems by proposing Adaptive Subgoal Search (AdaSubS), a method that adaptively adjusts planning horizons using diverse subgoals and verification, resulting in significant performance improvements over hierarchical planning algorithms on Sokoban, Rubik's Cube, and INT benchmarks.
Complex reasoning problems contain states that vary in the computational cost required to determine a good action plan. Taking advantage of this property, we propose Adaptive Subgoal Search (AdaSubS), a search method that adaptively adjusts the planning horizon. To this end, AdaSubS generates diverse sets of subgoals at different distances. A verification mechanism is employed to filter out unreachable subgoals swiftly, allowing to focus on feasible further subgoals. In this way, AdaSubS benefits from the efficiency of planning with longer subgoals and the fine control with the shorter ones, and thus scales well to difficult planning problems. We show that AdaSubS significantly surpasses hierarchical planning algorithms on three complex reasoning tasks: Sokoban, the Rubik's Cube, and inequality proving benchmark INT.